Overview

Dataset statistics

Number of variables22
Number of observations3023698
Missing cells4163552
Missing cells (%)6.3%
Duplicate rows16
Duplicate rows (%)< 0.1%
Total size in memory3.2 GiB
Average record size in memory1.1 KiB

Variable types

Categorical5
Text12
Numeric5

Alerts

Dataset has 16 (< 0.1%) duplicate rowsDuplicates
Sex is highly imbalanced (67.3%)Imbalance
PresentState is highly imbalanced (94.0%)Imbalance
PermanentAddress is highly imbalanced (> 99.9%)Imbalance
PermanentState is highly imbalanced (94.0%)Imbalance
Caste has 1730187 (57.2%) missing valuesMissing
Profession has 1108333 (36.7%) missing valuesMissing
DOB has 1270791 (42.0%) missing valuesMissing
Person_No has 50590 (1.7%) missing valuesMissing
crime_no is highly skewed (γ1 = 128.1216994)Skewed
age has 909422 (30.1%) zerosZeros

Reproduction

Analysis started2024-04-14 05:15:11.810301
Analysis finished2024-04-14 05:23:20.197860
Duration8 minutes and 8.39 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

District_Name
Categorical

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size194.5 MiB
Bengaluru City
495040 
Belagavi Dist
 
143048
Shivamogga
 
123016
Mandya
 
122764
Tumakuru
 
112746
Other values (36)
2027084 

Length

Max length23
Median length18
Mean length10.433502
Min length3

Characters and Unicode

Total characters31547760
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBagalkot
2nd rowBagalkot
3rd rowBagalkot
4th rowBagalkot
5th rowBagalkot

Common Values

ValueCountFrequency (%)
Bengaluru City 495040
 
16.4%
Belagavi Dist 143048
 
4.7%
Shivamogga 123016
 
4.1%
Mandya 122764
 
4.1%
Tumakuru 112746
 
3.7%
Mysuru Dist 107398
 
3.6%
Hassan 101558
 
3.4%
Raichur 97365
 
3.2%
Bengaluru Dist 96773
 
3.2%
Chitradurga 96149
 
3.2%
Other values (31) 1527841
50.5%

Length

2024-04-14T10:53:20.767281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city 706866
16.8%
bengaluru 591976
 
14.1%
dist 347219
 
8.2%
belagavi 182544
 
4.3%
mysuru 153773
 
3.7%
shivamogga 123016
 
2.9%
mandya 122764
 
2.9%
kalaburagi 116423
 
2.8%
tumakuru 112746
 
2.7%
hassan 101558
 
2.4%
Other values (33) 1653285
39.3%

Most occurring characters

ValueCountFrequency (%)
a 5757239
18.2%
u 2652108
 
8.4%
i 2366543
 
7.5%
r 2330842
 
7.4%
g 1821785
 
5.8%
l 1552973
 
4.9%
n 1363154
 
4.3%
t 1340104
 
4.2%
1188472
 
3.8%
y 1147014
 
3.6%
Other values (30) 10027526
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31547760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5757239
18.2%
u 2652108
 
8.4%
i 2366543
 
7.5%
r 2330842
 
7.4%
g 1821785
 
5.8%
l 1552973
 
4.9%
n 1363154
 
4.3%
t 1340104
 
4.2%
1188472
 
3.8%
y 1147014
 
3.6%
Other values (30) 10027526
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31547760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5757239
18.2%
u 2652108
 
8.4%
i 2366543
 
7.5%
r 2330842
 
7.4%
g 1821785
 
5.8%
l 1552973
 
4.9%
n 1363154
 
4.3%
t 1340104
 
4.2%
1188472
 
3.8%
y 1147014
 
3.6%
Other values (30) 10027526
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31547760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5757239
18.2%
u 2652108
 
8.4%
i 2366543
 
7.5%
r 2330842
 
7.4%
g 1821785
 
5.8%
l 1552973
 
4.9%
n 1363154
 
4.3%
t 1340104
 
4.2%
1188472
 
3.8%
y 1147014
 
3.6%
Other values (30) 10027526
31.8%
Distinct1055
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size205.1 MiB
2024-04-14T10:53:21.384671image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length44
Median length31
Mean length14.109578
Min length3

Characters and Unicode

Total characters42663102
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAmengad PS
2nd rowAmengad PS
3rd rowAmengad PS
4th rowAmengad PS
5th rowAmengad PS
ValueCountFrequency (%)
ps 3005259
41.2%
rural 335796
 
4.6%
town 198483
 
2.7%
traffic 149999
 
2.1%
nagar 78444
 
1.1%
women 58673
 
0.8%
crime 37035
 
0.5%
k.r 30626
 
0.4%
city 28549
 
0.4%
layout 25273
 
0.3%
Other values (828) 3341378
45.8%
2024-04-14T10:53:22.391721image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6587340
15.4%
4336199
 
10.2%
S 3366423
 
7.9%
P 3137869
 
7.4%
r 2664427
 
6.2%
i 1955005
 
4.6%
l 1867981
 
4.4%
n 1834881
 
4.3%
u 1737413
 
4.1%
e 1336158
 
3.1%
Other values (44) 13839406
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42663102
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6587340
15.4%
4336199
 
10.2%
S 3366423
 
7.9%
P 3137869
 
7.4%
r 2664427
 
6.2%
i 1955005
 
4.6%
l 1867981
 
4.4%
n 1834881
 
4.3%
u 1737413
 
4.1%
e 1336158
 
3.1%
Other values (44) 13839406
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42663102
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6587340
15.4%
4336199
 
10.2%
S 3366423
 
7.9%
P 3137869
 
7.4%
r 2664427
 
6.2%
i 1955005
 
4.6%
l 1867981
 
4.4%
n 1834881
 
4.3%
u 1737413
 
4.1%
e 1336158
 
3.1%
Other values (44) 13839406
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42663102
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6587340
15.4%
4336199
 
10.2%
S 3366423
 
7.9%
P 3137869
 
7.4%
r 2664427
 
6.2%
i 1955005
 
4.6%
l 1867981
 
4.4%
n 1834881
 
4.3%
u 1737413
 
4.1%
e 1336158
 
3.1%
Other values (44) 13839406
32.4%

FIRNo
Text

Distinct13415
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size190.3 MiB
2024-04-14T10:53:23.118003image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters27213282
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3276 ?
Unique (%)0.1%

Sample

1st row0001/2016
2nd row0002/2016
3rd row0002/2016
4th row0002/2016
5th row0002/2016
ValueCountFrequency (%)
0038/2020 2283
 
0.1%
0047/2020 2152
 
0.1%
0004/2018 2149
 
0.1%
0045/2020 2110
 
0.1%
0001/2017 2098
 
0.1%
0021/2017 2080
 
0.1%
0058/2017 2072
 
0.1%
0019/2017 2070
 
0.1%
0004/2022 2062
 
0.1%
0039/2020 2055
 
0.1%
Other values (13405) 3002567
99.3%
2024-04-14T10:53:24.194354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8460433
31.1%
2 5847634
21.5%
1 3509544
12.9%
/ 3023698
 
11.1%
3 1168797
 
4.3%
7 1028759
 
3.8%
6 1022575
 
3.8%
8 942793
 
3.5%
9 840456
 
3.1%
4 744400
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27213282
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8460433
31.1%
2 5847634
21.5%
1 3509544
12.9%
/ 3023698
 
11.1%
3 1168797
 
4.3%
7 1028759
 
3.8%
6 1022575
 
3.8%
8 942793
 
3.5%
9 840456
 
3.1%
4 744400
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27213282
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8460433
31.1%
2 5847634
21.5%
1 3509544
12.9%
/ 3023698
 
11.1%
3 1168797
 
4.3%
7 1028759
 
3.8%
6 1022575
 
3.8%
8 942793
 
3.5%
9 840456
 
3.1%
4 744400
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27213282
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8460433
31.1%
2 5847634
21.5%
1 3509544
12.9%
/ 3023698
 
11.1%
3 1168797
 
4.3%
7 1028759
 
3.8%
6 1022575
 
3.8%
8 942793
 
3.5%
9 840456
 
3.1%
4 744400
 
2.7%

Year
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.3978
Minimum2016
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 MiB
2024-04-14T10:53:24.634541image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2019
Q32022
95-th percentile2023
Maximum2024
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.4351763
Coefficient of variation (CV)0.0012058923
Kurtosis-1.2730259
Mean2019.3978
Median Absolute Deviation (MAD)2
Skewness0.15058158
Sum6.1060492 × 109
Variance5.9300835
MonotonicityNot monotonic
2024-04-14T10:53:24.965391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2017 459847
15.2%
2016 434226
14.4%
2018 392361
13.0%
2023 371624
12.3%
2022 340936
11.3%
2021 336134
11.1%
2020 317482
10.5%
2019 307362
10.2%
2024 63726
 
2.1%
ValueCountFrequency (%)
2016 434226
14.4%
2017 459847
15.2%
2018 392361
13.0%
2019 307362
10.2%
2020 317482
10.5%
2021 336134
11.1%
2022 340936
11.3%
2023 371624
12.3%
2024 63726
 
2.1%
ValueCountFrequency (%)
2024 63726
 
2.1%
2023 371624
12.3%
2022 340936
11.3%
2021 336134
11.1%
2020 317482
10.5%
2019 307362
10.2%
2018 392361
13.0%
2017 459847
15.2%
2016 434226
14.4%

Month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.260441
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 MiB
2024-04-14T10:53:25.328710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4217171
Coefficient of variation (CV)0.54656166
Kurtosis-1.1995829
Mean6.260441
Median Absolute Deviation (MAD)3
Skewness0.098270643
Sum18929683
Variance11.708148
MonotonicityNot monotonic
2024-04-14T10:53:25.677529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 289025
9.6%
4 276721
9.2%
2 270022
8.9%
1 261717
8.7%
5 257124
8.5%
6 255316
8.4%
7 250451
8.3%
8 243077
8.0%
10 237522
7.9%
9 233130
7.7%
Other values (2) 449593
14.9%
ValueCountFrequency (%)
1 261717
8.7%
2 270022
8.9%
3 289025
9.6%
4 276721
9.2%
5 257124
8.5%
6 255316
8.4%
7 250451
8.3%
8 243077
8.0%
9 233130
7.7%
10 237522
7.9%
ValueCountFrequency (%)
12 219761
7.3%
11 229832
7.6%
10 237522
7.9%
9 233130
7.7%
8 243077
8.0%
7 250451
8.3%
6 255316
8.4%
5 257124
8.5%
4 276721
9.2%
3 289025
9.6%
Distinct429975
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Memory size196.1 MiB
2024-04-14T10:53:27.594994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.999985
Min length5

Characters and Unicode

Total characters33260633
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique251711 ?
Unique (%)8.3%

Sample

1st rowYAMxxxxxDAR
2nd rowMAHxxxxxGER
3rd rowHANxxxxxALI
4th rowKASxxxxxADI
5th rowRAMxxxxxDAR
ValueCountFrequency (%)
s 30936
 
0.9%
m 27068
 
0.8%
k 26152
 
0.8%
r 23572
 
0.7%
unkxxxxxown 20060
 
0.6%
n 19439
 
0.6%
manxxxxxtha 17168
 
0.5%
b 14613
 
0.4%
p 13255
 
0.4%
v 13020
 
0.4%
Other values (257124) 3085318
93.8%
2024-04-14T10:53:28.470777image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
x 15126523
45.5%
a 2194616
 
6.6%
A 1645258
 
4.9%
S 843782
 
2.5%
R 767743
 
2.3%
h 706744
 
2.1%
r 671575
 
2.0%
i 653636
 
2.0%
n 648183
 
1.9%
M 636522
 
1.9%
Other values (58) 9366051
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33260633
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
x 15126523
45.5%
a 2194616
 
6.6%
A 1645258
 
4.9%
S 843782
 
2.5%
R 767743
 
2.3%
h 706744
 
2.1%
r 671575
 
2.0%
i 653636
 
2.0%
n 648183
 
1.9%
M 636522
 
1.9%
Other values (58) 9366051
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33260633
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
x 15126523
45.5%
a 2194616
 
6.6%
A 1645258
 
4.9%
S 843782
 
2.5%
R 767743
 
2.3%
h 706744
 
2.1%
r 671575
 
2.0%
i 653636
 
2.0%
n 648183
 
1.9%
M 636522
 
1.9%
Other values (58) 9366051
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33260633
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
x 15126523
45.5%
a 2194616
 
6.6%
A 1645258
 
4.9%
S 843782
 
2.5%
R 767743
 
2.3%
h 706744
 
2.1%
r 671575
 
2.0%
i 653636
 
2.0%
n 648183
 
1.9%
M 636522
 
1.9%
Other values (58) 9366051
28.2%
Distinct1439291
Distinct (%)47.6%
Missing11
Missing (%)< 0.1%
Memory size207.5 MiB
2024-04-14T10:53:32.208197image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length50
Median length43
Mean length14.942438
Min length1

Characters and Unicode

Total characters45181257
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1273277 ?
Unique (%)42.1%

Sample

1st rowYAMANAPPA DURAGAPPA VADDAR
2nd rowMAHANTESH VITTAPPA BADIGER
3rd rowHANAMANTH HANAMAPPA LAGALI
4th rowKASINATH SHEKARAPPA MUNDEVADI
5th rowRAMESH DURAGAPPA WADDAR
ValueCountFrequency (%)
of 94828
 
1.5%
so 90572
 
1.4%
kumar 63479
 
1.0%
driver 63428
 
1.0%
s 55121
 
0.9%
k 49131
 
0.8%
unknown 47399
 
0.7%
m 45317
 
0.7%
smt 39433
 
0.6%
no 34511
 
0.5%
Other values (380684) 5811964
90.9%
2024-04-14T10:53:34.022482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5363477
 
11.9%
A 4289698
 
9.5%
3445286
 
7.6%
S 1583853
 
3.5%
n 1525714
 
3.4%
N 1458795
 
3.2%
R 1426013
 
3.2%
h 1408460
 
3.1%
r 1394218
 
3.1%
i 1279974
 
2.8%
Other values (65) 22005769
48.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45181257
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5363477
 
11.9%
A 4289698
 
9.5%
3445286
 
7.6%
S 1583853
 
3.5%
n 1525714
 
3.4%
N 1458795
 
3.2%
R 1426013
 
3.2%
h 1408460
 
3.1%
r 1394218
 
3.1%
i 1279974
 
2.8%
Other values (65) 22005769
48.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45181257
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5363477
 
11.9%
A 4289698
 
9.5%
3445286
 
7.6%
S 1583853
 
3.5%
n 1525714
 
3.4%
N 1458795
 
3.2%
R 1426013
 
3.2%
h 1408460
 
3.1%
r 1394218
 
3.1%
i 1279974
 
2.8%
Other values (65) 22005769
48.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45181257
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5363477
 
11.9%
A 4289698
 
9.5%
3445286
 
7.6%
S 1583853
 
3.5%
n 1525714
 
3.4%
N 1458795
 
3.2%
R 1426013
 
3.2%
h 1408460
 
3.1%
r 1394218
 
3.1%
i 1279974
 
2.8%
Other values (65) 22005769
48.7%

age
Real number (ℝ)

ZEROS 

Distinct174
Distinct (%)< 0.1%
Missing9
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean25.507402
Minimum0
Maximum956
Zeros909422
Zeros (%)30.1%
Negative0
Negative (%)0.0%
Memory size23.1 MiB
2024-04-14T10:53:34.341053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median28
Q340
95-th percentile57
Maximum956
Range956
Interquartile range (IQR)40

Descriptive statistics

Standard deviation19.692743
Coefficient of variation (CV)0.77204034
Kurtosis2.8685183
Mean25.507402
Median Absolute Deviation (MAD)14
Skewness0.19323533
Sum77126451
Variance387.80414
MonotonicityNot monotonic
2024-04-14T10:53:34.724865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 909422
30.1%
30 117899
 
3.9%
35 115448
 
3.8%
40 103115
 
3.4%
28 92173
 
3.0%
45 91910
 
3.0%
25 88361
 
2.9%
32 84537
 
2.8%
38 73441
 
2.4%
26 70855
 
2.3%
Other values (164) 1276528
42.2%
ValueCountFrequency (%)
0 909422
30.1%
1 153
 
< 0.1%
2 56
 
< 0.1%
3 22
 
< 0.1%
4 19
 
< 0.1%
5 17
 
< 0.1%
6 15
 
< 0.1%
7 11
 
< 0.1%
8 18
 
< 0.1%
9 20
 
< 0.1%
ValueCountFrequency (%)
956 1
< 0.1%
689 1
< 0.1%
548 1
< 0.1%
545 1
< 0.1%
543 1
< 0.1%
476 1
< 0.1%
451 1
< 0.1%
444 1
< 0.1%
410 1
< 0.1%
408 1
< 0.1%

Caste
Text

MISSING 

Distinct981
Distinct (%)0.1%
Missing1730187
Missing (%)57.2%
Memory size132.9 MiB
2024-04-14T10:53:35.255759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length52
Median length43
Mean length7.9105133
Min length1

Characters and Unicode

Total characters10232336
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)< 0.1%

Sample

1st rowVADDA
2nd rowACHARI
3rd rowNEELAGAR
4th rowRADDY
5th rowKURUHINA SETTY
ValueCountFrequency (%)
muslim 216586
 
14.4%
lingayath 127932
 
8.5%
vokkaliga 124752
 
8.3%
adi 93193
 
6.2%
achari 87871
 
5.9%
karnataka 82268
 
5.5%
nayaka 52378
 
3.5%
kuruba 47061
 
3.1%
lambani 39420
 
2.6%
valmiki 23614
 
1.6%
Other values (1058) 605907
40.4%
2024-04-14T10:53:36.173834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2044621
20.0%
I 863675
 
8.4%
L 702105
 
6.9%
K 646605
 
6.3%
M 623919
 
6.1%
R 494007
 
4.8%
U 444025
 
4.3%
a 315084
 
3.1%
N 312504
 
3.1%
G 306436
 
3.0%
Other values (46) 3479355
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10232336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2044621
20.0%
I 863675
 
8.4%
L 702105
 
6.9%
K 646605
 
6.3%
M 623919
 
6.1%
R 494007
 
4.8%
U 444025
 
4.3%
a 315084
 
3.1%
N 312504
 
3.1%
G 306436
 
3.0%
Other values (46) 3479355
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10232336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2044621
20.0%
I 863675
 
8.4%
L 702105
 
6.9%
K 646605
 
6.3%
M 623919
 
6.1%
R 494007
 
4.8%
U 444025
 
4.3%
a 315084
 
3.1%
N 312504
 
3.1%
G 306436
 
3.0%
Other values (46) 3479355
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10232336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2044621
20.0%
I 863675
 
8.4%
L 702105
 
6.9%
K 646605
 
6.3%
M 623919
 
6.1%
R 494007
 
4.8%
U 444025
 
4.3%
a 315084
 
3.1%
N 312504
 
3.1%
G 306436
 
3.0%
Other values (46) 3479355
34.0%

Profession
Text

MISSING 

Distinct212
Distinct (%)< 0.1%
Missing1108333
Missing (%)36.7%
Memory size156.4 MiB
2024-04-14T10:53:36.725318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length41
Median length34
Mean length10.109253
Min length3

Characters and Unicode

Total characters19362909
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st rowLabourer
2nd rowDriver
3rd rowDriver
4th rowDriver
5th rowDriver
ValueCountFrequency (%)
farmer 503381
19.5%
labourer 423035
16.4%
driver 219718
8.5%
others 197361
 
7.6%
pi 172618
 
6.7%
specify 172618
 
6.7%
businessman 136760
 
5.3%
housewife 115265
 
4.5%
employed 55227
 
2.1%
38051
 
1.5%
Other values (262) 547659
21.2%
2024-04-14T10:53:37.769249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 2859093
14.8%
e 2392534
12.4%
2132236
 
11.0%
a 1337602
 
6.9%
i 920689
 
4.8%
s 850892
 
4.4%
o 822130
 
4.2%
u 791141
 
4.1%
m 768862
 
4.0%
F 520430
 
2.7%
Other values (47) 5967300
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19362909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 2859093
14.8%
e 2392534
12.4%
2132236
 
11.0%
a 1337602
 
6.9%
i 920689
 
4.8%
s 850892
 
4.4%
o 822130
 
4.2%
u 791141
 
4.1%
m 768862
 
4.0%
F 520430
 
2.7%
Other values (47) 5967300
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19362909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 2859093
14.8%
e 2392534
12.4%
2132236
 
11.0%
a 1337602
 
6.9%
i 920689
 
4.8%
s 850892
 
4.4%
o 822130
 
4.2%
u 791141
 
4.1%
m 768862
 
4.0%
F 520430
 
2.7%
Other values (47) 5967300
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19362909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 2859093
14.8%
e 2392534
12.4%
2132236
 
11.0%
a 1337602
 
6.9%
i 920689
 
4.8%
s 850892
 
4.4%
o 822130
 
4.2%
u 791141
 
4.1%
m 768862
 
4.0%
F 520430
 
2.7%
Other values (47) 5967300
30.8%

Sex
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing38
Missing (%)< 0.1%
Memory size176.6 MiB
MALE
2677021 
FEMALE
345090 
Enuch
 
1549

Length

Max length6
Median length4
Mean length4.2287721
Min length4

Characters and Unicode

Total characters12786369
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowMALE
3rd rowMALE
4th rowMALE
5th rowMALE

Common Values

ValueCountFrequency (%)
MALE 2677021
88.5%
FEMALE 345090
 
11.4%
Enuch 1549
 
0.1%
(Missing) 38
 
< 0.1%

Length

2024-04-14T10:53:38.178263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T10:53:38.635679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
male 2677021
88.5%
female 345090
 
11.4%
enuch 1549
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 3368750
26.3%
M 3022111
23.6%
A 3022111
23.6%
L 3022111
23.6%
F 345090
 
2.7%
n 1549
 
< 0.1%
u 1549
 
< 0.1%
c 1549
 
< 0.1%
h 1549
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12786369
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 3368750
26.3%
M 3022111
23.6%
A 3022111
23.6%
L 3022111
23.6%
F 345090
 
2.7%
n 1549
 
< 0.1%
u 1549
 
< 0.1%
c 1549
 
< 0.1%
h 1549
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12786369
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 3368750
26.3%
M 3022111
23.6%
A 3022111
23.6%
L 3022111
23.6%
F 345090
 
2.7%
n 1549
 
< 0.1%
u 1549
 
< 0.1%
c 1549
 
< 0.1%
h 1549
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12786369
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 3368750
26.3%
M 3022111
23.6%
A 3022111
23.6%
L 3022111
23.6%
F 345090
 
2.7%
n 1549
 
< 0.1%
u 1549
 
< 0.1%
c 1549
 
< 0.1%
h 1549
 
< 0.1%
Distinct1486814
Distinct (%)49.2%
Missing0
Missing (%)0.0%
Memory size266.5 MiB
2024-04-14T10:53:54.475683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length125
Median length82
Mean length35.416198
Min length1

Characters and Unicode

Total characters107087887
Distinct characters116
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1182533 ?
Unique (%)39.1%

Sample

1st rowHUVINAHALLI,TQ-HUNAGUND
2nd rowADIHAL NOW AT RAKKASAGI,TQ-HUNAGUND
3rd rowRAKKASAGI,TQ-HUNAGUND
4th rowHUNAGUND CHANNAMMA CIRCAL NEAR,TQ-HUNAGUND
5th rowMASKI GANDI NAGAR IB BACK,TQ-LINGASUR
ValueCountFrequency (%)
tq 532769
 
4.3%
taluk 337118
 
2.7%
village 262100
 
2.1%
r/o 217506
 
1.8%
no 172780
 
1.4%
cross 147800
 
1.2%
town 139072
 
1.1%
hobli 119067
 
1.0%
at 118145
 
1.0%
village,tq 116842
 
0.9%
Other values (1037360) 10147939
82.4%
2024-04-14T10:53:56.133044image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 10238147
 
9.6%
9442927
 
8.8%
A 6529455
 
6.1%
l 4642507
 
4.3%
, 4411565
 
4.1%
i 3766880
 
3.5%
r 3255148
 
3.0%
T 2948209
 
2.8%
e 2920682
 
2.7%
n 2825460
 
2.6%
Other values (106) 56106907
52.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 107087887
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10238147
 
9.6%
9442927
 
8.8%
A 6529455
 
6.1%
l 4642507
 
4.3%
, 4411565
 
4.1%
i 3766880
 
3.5%
r 3255148
 
3.0%
T 2948209
 
2.8%
e 2920682
 
2.7%
n 2825460
 
2.6%
Other values (106) 56106907
52.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 107087887
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10238147
 
9.6%
9442927
 
8.8%
A 6529455
 
6.1%
l 4642507
 
4.3%
, 4411565
 
4.1%
i 3766880
 
3.5%
r 3255148
 
3.0%
T 2948209
 
2.8%
e 2920682
 
2.7%
n 2825460
 
2.6%
Other values (106) 56106907
52.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 107087887
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10238147
 
9.6%
9442927
 
8.8%
A 6529455
 
6.1%
l 4642507
 
4.3%
, 4411565
 
4.1%
i 3766880
 
3.5%
r 3255148
 
3.0%
T 2948209
 
2.8%
e 2920682
 
2.7%
n 2825460
 
2.6%
Other values (106) 56106907
52.4%
Distinct738
Distinct (%)< 0.1%
Missing982
Missing (%)< 0.1%
Memory size193.9 MiB
2024-04-14T10:53:56.381104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length29
Median length23
Mean length10.241352
Min length3

Characters and Unicode

Total characters30956698
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st rowBagalkot
2nd rowBagalkot
3rd rowBagalkot
4th rowBagalkot
5th rowRaichur
ValueCountFrequency (%)
city 660270
 
15.9%
bengaluru 562764
 
13.5%
dist 339787
 
8.2%
belagavi 178355
 
4.3%
mysuru 151447
 
3.6%
shivamogga 122146
 
2.9%
mandya 117442
 
2.8%
kalaburagi 114915
 
2.8%
tumakuru 108818
 
2.6%
ballari 104665
 
2.5%
Other values (731) 1699662
40.9%
2024-04-14T10:53:57.235303image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5549040
17.9%
u 2598149
 
8.4%
r 2331396
 
7.5%
i 2317177
 
7.5%
g 1734628
 
5.6%
l 1602351
 
5.2%
n 1316917
 
4.3%
t 1307749
 
4.2%
1137669
 
3.7%
y 1046209
 
3.4%
Other values (48) 10015413
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30956698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5549040
17.9%
u 2598149
 
8.4%
r 2331396
 
7.5%
i 2317177
 
7.5%
g 1734628
 
5.6%
l 1602351
 
5.2%
n 1316917
 
4.3%
t 1307749
 
4.2%
1137669
 
3.7%
y 1046209
 
3.4%
Other values (48) 10015413
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30956698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5549040
17.9%
u 2598149
 
8.4%
r 2331396
 
7.5%
i 2317177
 
7.5%
g 1734628
 
5.6%
l 1602351
 
5.2%
n 1316917
 
4.3%
t 1307749
 
4.2%
1137669
 
3.7%
y 1046209
 
3.4%
Other values (48) 10015413
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30956698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5549040
17.9%
u 2598149
 
8.4%
r 2331396
 
7.5%
i 2317177
 
7.5%
g 1734628
 
5.6%
l 1602351
 
5.2%
n 1316917
 
4.3%
t 1307749
 
4.2%
1137669
 
3.7%
y 1046209
 
3.4%
Other values (48) 10015413
32.4%

PresentState
Categorical

IMBALANCE 

Distinct38
Distinct (%)< 0.1%
Missing808
Missing (%)< 0.1%
Memory size190.4 MiB
Karnataka
2926106 
Andhra pradesh
 
20679
Maharashtra
 
18023
Tamilnadu
 
15156
Kerala
 
8401
Other values (33)
 
34525

Length

Max length20
Median length9
Mean length9.0365329
Min length3

Characters and Unicode

Total characters27316445
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKarnataka
2nd rowKarnataka
3rd rowKarnataka
4th rowKarnataka
5th rowKarnataka

Common Values

ValueCountFrequency (%)
Karnataka 2926106
96.8%
Andhra pradesh 20679
 
0.7%
Maharashtra 18023
 
0.6%
Tamilnadu 15156
 
0.5%
Kerala 8401
 
0.3%
Rajasthan 8193
 
0.3%
Uttar pradesh 4594
 
0.2%
Telangana 3489
 
0.1%
Bihar 2562
 
0.1%
West bengal 2331
 
0.1%
Other values (28) 13356
 
0.4%

Length

2024-04-14T10:53:57.707823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
karnataka 2926106
95.8%
pradesh 27107
 
0.9%
andhra 20679
 
0.7%
maharashtra 18023
 
0.6%
tamilnadu 15156
 
0.5%
kerala 8401
 
0.3%
rajasthan 8193
 
0.3%
uttar 4594
 
0.2%
telangana 3489
 
0.1%
bihar 2562
 
0.1%
Other values (38) 19036
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 11935319
43.7%
r 3032473
 
11.1%
n 2983986
 
10.9%
t 2966724
 
10.9%
K 2934507
 
10.7%
k 2927595
 
10.7%
h 102397
 
0.4%
d 66224
 
0.2%
s 60119
 
0.2%
e 46144
 
0.2%
Other values (35) 260957
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27316445
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11935319
43.7%
r 3032473
 
11.1%
n 2983986
 
10.9%
t 2966724
 
10.9%
K 2934507
 
10.7%
k 2927595
 
10.7%
h 102397
 
0.4%
d 66224
 
0.2%
s 60119
 
0.2%
e 46144
 
0.2%
Other values (35) 260957
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27316445
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11935319
43.7%
r 3032473
 
11.1%
n 2983986
 
10.9%
t 2966724
 
10.9%
K 2934507
 
10.7%
k 2927595
 
10.7%
h 102397
 
0.4%
d 66224
 
0.2%
s 60119
 
0.2%
e 46144
 
0.2%
Other values (35) 260957
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27316445
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11935319
43.7%
r 3032473
 
11.1%
n 2983986
 
10.9%
t 2966724
 
10.9%
K 2934507
 
10.7%
k 2927595
 
10.7%
h 102397
 
0.4%
d 66224
 
0.2%
s 60119
 
0.2%
e 46144
 
0.2%
Other values (35) 260957
 
1.0%

PermanentAddress
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size167.3 MiB
,
3023694 
KITHANUR VILLAGE BIDARAHALLI(H),BANGALORE EAST TQ
 
2
KITHANUR VILLAGE BIDARAHALLI(H),BANGALORE EAST TQ
 
1
T-2,4TH BLOCK, 3RD FLOOR,MEDAR BLOCK, BAMBOO BAZAR
 
1

Length

Max length50
Median length1
Mean length1.0000642
Min length1

Characters and Unicode

Total characters3023892
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row,
2nd row,
3rd row,
4th row,
5th row,

Common Values

ValueCountFrequency (%)
, 3023694
> 99.9%
KITHANUR VILLAGE BIDARAHALLI(H),BANGALORE EAST TQ 2
 
< 0.1%
KITHANUR VILLAGE BIDARAHALLI(H),BANGALORE EAST TQ 1
 
< 0.1%
T-2,4TH BLOCK, 3RD FLOOR,MEDAR BLOCK, BAMBOO BAZAR 1
 
< 0.1%

Length

2024-04-14T10:53:58.119583image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T10:53:58.464918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3023694
> 99.9%
kithanur 3
 
< 0.1%
village 3
 
< 0.1%
bidarahalli(h),bangalore 3
 
< 0.1%
east 3
 
< 0.1%
tq 3
 
< 0.1%
block 2
 
< 0.1%
t-2,4th 1
 
< 0.1%
3rd 1
 
< 0.1%
floor,medar 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
, 3023701
> 99.9%
A 28
 
< 0.1%
19
 
< 0.1%
L 18
 
< 0.1%
R 13
 
< 0.1%
I 12
 
< 0.1%
B 11
 
< 0.1%
T 11
 
< 0.1%
H 10
 
< 0.1%
E 10
 
< 0.1%
Other values (19) 59
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3023892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 3023701
> 99.9%
A 28
 
< 0.1%
19
 
< 0.1%
L 18
 
< 0.1%
R 13
 
< 0.1%
I 12
 
< 0.1%
B 11
 
< 0.1%
T 11
 
< 0.1%
H 10
 
< 0.1%
E 10
 
< 0.1%
Other values (19) 59
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3023892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 3023701
> 99.9%
A 28
 
< 0.1%
19
 
< 0.1%
L 18
 
< 0.1%
R 13
 
< 0.1%
I 12
 
< 0.1%
B 11
 
< 0.1%
T 11
 
< 0.1%
H 10
 
< 0.1%
E 10
 
< 0.1%
Other values (19) 59
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3023892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 3023701
> 99.9%
A 28
 
< 0.1%
19
 
< 0.1%
L 18
 
< 0.1%
R 13
 
< 0.1%
I 12
 
< 0.1%
B 11
 
< 0.1%
T 11
 
< 0.1%
H 10
 
< 0.1%
E 10
 
< 0.1%
Other values (19) 59
 
< 0.1%
Distinct738
Distinct (%)< 0.1%
Missing982
Missing (%)< 0.1%
Memory size193.9 MiB
2024-04-14T10:53:58.817792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length29
Median length23
Mean length10.241352
Min length3

Characters and Unicode

Total characters30956698
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st rowBagalkot
2nd rowBagalkot
3rd rowBagalkot
4th rowBagalkot
5th rowRaichur
ValueCountFrequency (%)
city 660270
 
15.9%
bengaluru 562764
 
13.5%
dist 339787
 
8.2%
belagavi 178355
 
4.3%
mysuru 151447
 
3.6%
shivamogga 122146
 
2.9%
mandya 117442
 
2.8%
kalaburagi 114915
 
2.8%
tumakuru 108818
 
2.6%
ballari 104665
 
2.5%
Other values (731) 1699662
40.9%
2024-04-14T10:53:59.574933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5549040
17.9%
u 2598149
 
8.4%
r 2331396
 
7.5%
i 2317177
 
7.5%
g 1734628
 
5.6%
l 1602351
 
5.2%
n 1316917
 
4.3%
t 1307749
 
4.2%
1137669
 
3.7%
y 1046209
 
3.4%
Other values (48) 10015413
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30956698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5549040
17.9%
u 2598149
 
8.4%
r 2331396
 
7.5%
i 2317177
 
7.5%
g 1734628
 
5.6%
l 1602351
 
5.2%
n 1316917
 
4.3%
t 1307749
 
4.2%
1137669
 
3.7%
y 1046209
 
3.4%
Other values (48) 10015413
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30956698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5549040
17.9%
u 2598149
 
8.4%
r 2331396
 
7.5%
i 2317177
 
7.5%
g 1734628
 
5.6%
l 1602351
 
5.2%
n 1316917
 
4.3%
t 1307749
 
4.2%
1137669
 
3.7%
y 1046209
 
3.4%
Other values (48) 10015413
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30956698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5549040
17.9%
u 2598149
 
8.4%
r 2331396
 
7.5%
i 2317177
 
7.5%
g 1734628
 
5.6%
l 1602351
 
5.2%
n 1316917
 
4.3%
t 1307749
 
4.2%
1137669
 
3.7%
y 1046209
 
3.4%
Other values (48) 10015413
32.4%

PermanentState
Categorical

IMBALANCE 

Distinct38
Distinct (%)< 0.1%
Missing808
Missing (%)< 0.1%
Memory size190.4 MiB
Karnataka
2926106 
Andhra pradesh
 
20679
Maharashtra
 
18023
Tamilnadu
 
15156
Kerala
 
8401
Other values (33)
 
34525

Length

Max length20
Median length9
Mean length9.0365329
Min length3

Characters and Unicode

Total characters27316445
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKarnataka
2nd rowKarnataka
3rd rowKarnataka
4th rowKarnataka
5th rowKarnataka

Common Values

ValueCountFrequency (%)
Karnataka 2926106
96.8%
Andhra pradesh 20679
 
0.7%
Maharashtra 18023
 
0.6%
Tamilnadu 15156
 
0.5%
Kerala 8401
 
0.3%
Rajasthan 8193
 
0.3%
Uttar pradesh 4594
 
0.2%
Telangana 3489
 
0.1%
Bihar 2562
 
0.1%
West bengal 2331
 
0.1%
Other values (28) 13356
 
0.4%

Length

2024-04-14T10:54:00.030761image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
karnataka 2926106
95.8%
pradesh 27107
 
0.9%
andhra 20679
 
0.7%
maharashtra 18023
 
0.6%
tamilnadu 15156
 
0.5%
kerala 8401
 
0.3%
rajasthan 8193
 
0.3%
uttar 4594
 
0.2%
telangana 3489
 
0.1%
bihar 2562
 
0.1%
Other values (38) 19036
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 11935319
43.7%
r 3032473
 
11.1%
n 2983986
 
10.9%
t 2966724
 
10.9%
K 2934507
 
10.7%
k 2927595
 
10.7%
h 102397
 
0.4%
d 66224
 
0.2%
s 60119
 
0.2%
e 46144
 
0.2%
Other values (35) 260957
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27316445
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11935319
43.7%
r 3032473
 
11.1%
n 2983986
 
10.9%
t 2966724
 
10.9%
K 2934507
 
10.7%
k 2927595
 
10.7%
h 102397
 
0.4%
d 66224
 
0.2%
s 60119
 
0.2%
e 46144
 
0.2%
Other values (35) 260957
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27316445
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11935319
43.7%
r 3032473
 
11.1%
n 2983986
 
10.9%
t 2966724
 
10.9%
K 2934507
 
10.7%
k 2927595
 
10.7%
h 102397
 
0.4%
d 66224
 
0.2%
s 60119
 
0.2%
e 46144
 
0.2%
Other values (35) 260957
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27316445
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11935319
43.7%
r 3032473
 
11.1%
n 2983986
 
10.9%
t 2966724
 
10.9%
K 2934507
 
10.7%
k 2927595
 
10.7%
h 102397
 
0.4%
d 66224
 
0.2%
s 60119
 
0.2%
e 46144
 
0.2%
Other values (35) 260957
 
1.0%
Distinct128
Distinct (%)< 0.1%
Missing13
Missing (%)< 0.1%
Memory size178.8 MiB
2024-04-14T10:54:00.413925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length30
Median length5
Mean length5.003165
Min length3

Characters and Unicode

Total characters15127995
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)< 0.1%

Sample

1st rowIndia
2nd rowIndia
3rd rowIndia
4th rowIndia
5th rowIndia
ValueCountFrequency (%)
india 3019603
99.9%
indonesia 758
 
< 0.1%
nigeria 536
 
< 0.1%
nepal 294
 
< 0.1%
iceland 289
 
< 0.1%
iran 241
 
< 0.1%
bangladesh 240
 
< 0.1%
oman 211
 
< 0.1%
uganda 137
 
< 0.1%
denmark 114
 
< 0.1%
Other values (137) 1621
 
0.1%
2024-04-14T10:54:01.329103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3024453
20.0%
n 3023312
20.0%
i 3022367
20.0%
d 3021350
20.0%
I 3021044
20.0%
e 2750
 
< 0.1%
r 1368
 
< 0.1%
o 1232
 
< 0.1%
s 1201
 
< 0.1%
g 1117
 
< 0.1%
Other values (43) 7801
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15127995
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3024453
20.0%
n 3023312
20.0%
i 3022367
20.0%
d 3021350
20.0%
I 3021044
20.0%
e 2750
 
< 0.1%
r 1368
 
< 0.1%
o 1232
 
< 0.1%
s 1201
 
< 0.1%
g 1117
 
< 0.1%
Other values (43) 7801
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15127995
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3024453
20.0%
n 3023312
20.0%
i 3022367
20.0%
d 3021350
20.0%
I 3021044
20.0%
e 2750
 
< 0.1%
r 1368
 
< 0.1%
o 1232
 
< 0.1%
s 1201
 
< 0.1%
g 1117
 
< 0.1%
Other values (43) 7801
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15127995
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3024453
20.0%
n 3023312
20.0%
i 3022367
20.0%
d 3021350
20.0%
I 3021044
20.0%
e 2750
 
< 0.1%
r 1368
 
< 0.1%
o 1232
 
< 0.1%
s 1201
 
< 0.1%
g 1117
 
< 0.1%
Other values (43) 7801
 
0.1%

DOB
Text

MISSING 

Distinct9435
Distinct (%)0.5%
Missing1270791
Missing (%)42.0%
Memory size172.5 MiB
2024-04-14T10:54:01.957554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters40316861
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5134 ?
Unique (%)0.3%

Sample

1st row9999-01-01 00:00:00.000
2nd row9999-01-01 00:00:00.000
3rd row9999-01-01 00:00:00.000
4th row9999-01-01 00:00:00.000
5th row9999-01-01 00:00:00.000
ValueCountFrequency (%)
00:00:00.000 1752907
50.0%
9999-01-01 1728930
49.3%
1985-01-01 240
 
< 0.1%
1989-01-01 170
 
< 0.1%
1990-01-01 159
 
< 0.1%
1993-01-01 156
 
< 0.1%
1987-01-01 149
 
< 0.1%
1986-01-01 146
 
< 0.1%
1991-01-01 143
 
< 0.1%
1992-01-01 141
 
< 0.1%
Other values (9426) 22673
 
0.6%
2024-04-14T10:54:02.854978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19280900
47.8%
9 6948856
 
17.2%
- 3505814
 
8.7%
: 3505814
 
8.7%
1 3505718
 
8.7%
1752907
 
4.3%
. 1752907
 
4.3%
2 14204
 
< 0.1%
8 11783
 
< 0.1%
6 9333
 
< 0.1%
Other values (4) 28625
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40316861
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19280900
47.8%
9 6948856
 
17.2%
- 3505814
 
8.7%
: 3505814
 
8.7%
1 3505718
 
8.7%
1752907
 
4.3%
. 1752907
 
4.3%
2 14204
 
< 0.1%
8 11783
 
< 0.1%
6 9333
 
< 0.1%
Other values (4) 28625
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40316861
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19280900
47.8%
9 6948856
 
17.2%
- 3505814
 
8.7%
: 3505814
 
8.7%
1 3505718
 
8.7%
1752907
 
4.3%
. 1752907
 
4.3%
2 14204
 
< 0.1%
8 11783
 
< 0.1%
6 9333
 
< 0.1%
Other values (4) 28625
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40316861
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19280900
47.8%
9 6948856
 
17.2%
- 3505814
 
8.7%
: 3505814
 
8.7%
1 3505718
 
8.7%
1752907
 
4.3%
. 1752907
 
4.3%
2 14204
 
< 0.1%
8 11783
 
< 0.1%
6 9333
 
< 0.1%
Other values (4) 28625
 
0.1%

Person_No
Text

MISSING 

Distinct463
Distinct (%)< 0.1%
Missing50590
Missing (%)1.7%
Memory size169.0 MiB
2024-04-14T10:54:03.549521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.0595562
Min length1

Characters and Unicode

Total characters6123283
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique161 ?
Unique (%)< 0.1%

Sample

1st rowA1
2nd rowA2
3rd rowA1
4th rowA4
5th rowA5
ValueCountFrequency (%)
a1 1223125
41.1%
a2 519411
17.5%
a3 340854
 
11.5%
a4 240524
 
8.1%
a5 165038
 
5.6%
a6 113552
 
3.8%
a7 79240
 
2.7%
a8 56834
 
1.9%
a9 40890
 
1.4%
a10 30799
 
1.0%
Other values (449) 162844
 
5.5%
2024-04-14T10:54:04.601178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2954310
48.2%
1 1391188
22.7%
2 570010
 
9.3%
3 371138
 
6.1%
4 262574
 
4.3%
5 182324
 
3.0%
6 127532
 
2.1%
7 90891
 
1.5%
8 66708
 
1.1%
9 49447
 
0.8%
Other values (8) 57161
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6123283
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2954310
48.2%
1 1391188
22.7%
2 570010
 
9.3%
3 371138
 
6.1%
4 262574
 
4.3%
5 182324
 
3.0%
6 127532
 
2.1%
7 90891
 
1.5%
8 66708
 
1.1%
9 49447
 
0.8%
Other values (8) 57161
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6123283
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2954310
48.2%
1 1391188
22.7%
2 570010
 
9.3%
3 371138
 
6.1%
4 262574
 
4.3%
5 182324
 
3.0%
6 127532
 
2.1%
7 90891
 
1.5%
8 66708
 
1.1%
9 49447
 
0.8%
Other values (8) 57161
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6123283
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2954310
48.2%
1 1391188
22.7%
2 570010
 
9.3%
3 371138
 
6.1%
4 262574
 
4.3%
5 182324
 
3.0%
6 127532
 
2.1%
7 90891
 
1.5%
8 66708
 
1.1%
9 49447
 
0.8%
Other values (8) 57161
 
0.9%

Arr_ID
Real number (ℝ)

Distinct31589
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0194425 × 109
Minimum2.0030001 × 109
Maximum2.0240011 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 MiB
2024-04-14T10:54:04.874644image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2.0030001 × 109
5-th percentile2.0160004 × 109
Q12.0170009 × 109
median2.019001 × 109
Q32.0220001 × 109
95-th percentile2.023001 × 109
Maximum2.0240011 × 109
Range21000947
Interquartile range (IQR)4999236

Descriptive statistics

Standard deviation2437632.9
Coefficient of variation (CV)0.0012070821
Kurtosis-1.2719993
Mean2.0194425 × 109
Median Absolute Deviation (MAD)2000359
Skewness0.13517617
Sum6.1061842 × 1015
Variance5.9420544 × 1012
MonotonicityNot monotonic
2024-04-14T10:54:05.065840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2018000090 530
 
< 0.1%
2018000089 530
 
< 0.1%
2018000130 523
 
< 0.1%
2018000092 523
 
< 0.1%
2018000088 522
 
< 0.1%
2018000111 519
 
< 0.1%
2018000095 517
 
< 0.1%
2018000117 516
 
< 0.1%
2018000123 516
 
< 0.1%
2018000091 514
 
< 0.1%
Other values (31579) 3018488
99.8%
ValueCountFrequency (%)
2003000110 1
 
< 0.1%
2003000111 1
 
< 0.1%
2003000112 1
 
< 0.1%
2016000001 421
< 0.1%
2016000002 353
< 0.1%
2016000003 378
< 0.1%
2016000004 400
< 0.1%
2016000005 401
< 0.1%
2016000006 418
< 0.1%
2016000007 416
< 0.1%
ValueCountFrequency (%)
2024001057 1
< 0.1%
2024001056 1
< 0.1%
2024001051 1
< 0.1%
2024001049 1
< 0.1%
2024001047 1
< 0.1%
2024001046 1
< 0.1%
2024001039 1
< 0.1%
2024001038 1
< 0.1%
2024001037 1
< 0.1%
2024000987 1
< 0.1%

crime_no
Real number (ℝ)

SKEWED 

Distinct1276540
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0532578 × 1016
Minimum1.043801 × 1016
Maximum1.044322 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 MiB
2024-04-14T10:54:05.288331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1.043801 × 1016
5-th percentile1.0439016 × 1016
Q11.044322 × 1016
median1.0454159 × 1016
Q31.046411 × 1016
95-th percentile1.097816 × 1016
Maximum1.044322 × 1017
Range9.399419 × 1016
Interquartile range (IQR)2.08902 × 1013

Descriptive statistics

Standard deviation6.7832706 × 1014
Coefficient of variation (CV)0.064402757
Kurtosis17727.252
Mean1.0532578 × 1016
Median Absolute Deviation (MAD)1.00126 × 1013
Skewness128.1217
Sum8.254777 × 1018
Variance4.6012761 × 1029
MonotonicityNot monotonic
2024-04-14T10:54:05.492749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.044414632 × 1016289
 
< 0.1%
1.044321992 × 1016199
 
< 0.1%
1.044414632 × 1016196
 
< 0.1%
1.044414832 × 1016164
 
< 0.1%
1.044314052 × 1016160
 
< 0.1%
1.044318742 × 1016143
 
< 0.1%
1.046711212 × 1016142
 
< 0.1%
1.045215352 × 1016140
 
< 0.1%
1.045415702 × 1016139
 
< 0.1%
1.044318002 × 1016139
 
< 0.1%
Other values (1276530) 3021987
99.9%
ValueCountFrequency (%)
1.043800972 × 10161
 
< 0.1%
1.043800972 × 10161
 
< 0.1%
1.043800972 × 10161
 
< 0.1%
1.043800972 × 10164
< 0.1%
1.043800972 × 10161
 
< 0.1%
1.043800972 × 10162
< 0.1%
1.043800972 × 10164
< 0.1%
1.043800972 × 10161
 
< 0.1%
1.043800972 × 10163
< 0.1%
1.043800972 × 10162
< 0.1%
ValueCountFrequency (%)
1.044322002 × 10172
< 0.1%
1.044322002 × 10173
< 0.1%
1.044322002 × 10171
 
< 0.1%
1.044322002 × 10172
< 0.1%
1.044322002 × 10173
< 0.1%
1.044322002 × 10173
< 0.1%
1.044322002 × 10173
< 0.1%
1.044322002 × 10171
 
< 0.1%
1.044322002 × 10171
 
< 0.1%
1.044322002 × 10173
< 0.1%

Interactions

2024-04-14T10:52:06.469904image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:51:41.624158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:51:47.620133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:51:53.457844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:52:00.227454image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:52:07.563187image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:51:42.977890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:51:48.665790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:51:54.969219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:52:01.540571image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:52:08.737257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:51:44.147568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:51:49.962237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:51:56.235015image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:52:02.901338image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:52:09.765793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:51:45.314695image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:51:51.127176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:51:57.657737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:52:04.132504image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:52:10.762155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:51:46.488928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:51:52.167382image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:51:59.094793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:52:05.287925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-04-14T10:52:15.226140image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T10:52:26.376814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

District_NameUnitNameFIRNoYearMonthAccusedNamePerson_NameageCasteProfessionSexPresentAddressPresentCityPresentStatePermanentAddressPermanentCityPermanentStateNationality_NameDOBPerson_NoArr_IDcrime_no
0BagalkotAmengad PS0001/201620161YAMxxxxxDARYAMANAPPA DURAGAPPA VADDAR26.0VADDALabourerMALEHUVINAHALLI,TQ-HUNAGUNDBagalkotKarnataka,BagalkotKarnatakaIndia9999-01-01 00:00:00.000A1201600000310470124520160001
1BagalkotAmengad PS0002/201620161MAHxxxxxGERMAHANTESH VITTAPPA BADIGER27.0NaNNaNMALEADIHAL NOW AT RAKKASAGI,TQ-HUNAGUNDBagalkotKarnataka,BagalkotKarnatakaIndia9999-01-01 00:00:00.000A2201600000910470124520160002
2BagalkotAmengad PS0002/201620161HANxxxxxALIHANAMANTH HANAMAPPA LAGALI25.0NaNNaNMALERAKKASAGI,TQ-HUNAGUNDBagalkotKarnataka,BagalkotKarnatakaIndia9999-01-01 00:00:00.000A1201600001010470124520160002
3BagalkotAmengad PS0002/201620161KASxxxxxADIKASINATH SHEKARAPPA MUNDEVADI26.0NaNNaNMALEHUNAGUND CHANNAMMA CIRCAL NEAR,TQ-HUNAGUNDBagalkotKarnataka,BagalkotKarnatakaIndia9999-01-01 00:00:00.000A4201600001110470124520160002
4BagalkotAmengad PS0002/201620161RAMxxxxxDARRAMESH DURAGAPPA WADDAR23.0NaNNaNMALEMASKI GANDI NAGAR IB BACK,TQ-LINGASURRaichurKarnataka,RaichurKarnatakaIndia9999-01-01 00:00:00.000A5201600000710470124520160002
5BagalkotAmengad PS0002/201620161KALxxxxxOVIKALAKAPPA CHANDAPPA BHOVI38.0NaNNaNMALEGADACHINTI NOW AT RAKKASAGI,TQ-KUSTAGIKoppalKarnataka,KoppalKarnatakaIndia9999-01-01 00:00:00.000A3201600000810470124520160002
6BagalkotAmengad PS0003/201620161SHAxxxxxDINSHANKRAPPA NAGAPPA AGASIMUNDIN30.0NaNNaNMALERAKKASAGI,TQ-HUNAGUNDBagalkotKarnataka,BagalkotKarnatakaIndia9999-01-01 00:00:00.000A1201600001210470124520160003
7BagalkotAmengad PS0004/201620161HUSxxxxxWARHUSENAPPA YANKAPPA TALAWAR30.0ACHARIDriverMALEJAMBALADINNI,TQ-HUNAGUNDBagalkotKarnataka,BagalkotKarnatakaIndiaNaNA1201600001710470124520160004
8BagalkotAmengad PS0005/201620161SHAxxxxxANISHANKAR GOVINDAPPA NEELAVANI38.0NEELAGARDriverMALEGULEDAGUDDA BUS DEPOT,TQ-BADAMIBagalkotKarnataka,BagalkotKarnatakaIndia9999-01-01 00:00:00.000A1201600001810470124520160005
9BagalkotAmengad PS0006/201620161SHIxxxxxOORSHIVANAGOUDA MALAKAJAPPA KODAGANOOR35.0RADDYDriverMALEILKAL BUS DEPOT,TQ-HUNGUNDBagalkotKarnataka,BagalkotKarnatakaIndia9999-01-01 00:00:00.000A1201600002010470124520160006
District_NameUnitNameFIRNoYearMonthAccusedNamePerson_NameageCasteProfessionSexPresentAddressPresentCityPresentStatePermanentAddressPermanentCityPermanentStateNationality_NameDOBPerson_NoArr_IDcrime_no
3023688YadgirYadgiri Women PS0043/2023202311AnjxxxxxppaAnjappa0.0NaNNaNMALER/o Koiluru,tq dist YadgiriYadgirKarnataka,YadgirKarnatakaIndia9999-01-01 00:00:00.000A1202300020110978218320230043
3023689YadgirYadgiri Women PS0001/202420241RAVxxxxxDAVRAVI NANU JADAV0.0NaNFarmerMALESAMANAPURA DODDA THANDA,YadgirKarnataka,YadgirKarnatakaIndia9999-01-01 00:00:00.000A1202400000210978218320240001
3023690YadgirYadgiri Women PS0003/202420241KasxxxxxppaKashappa25.0MADIGALabourerMALER/o Talak Village,tq dist yadgiriYadgirKarnataka,YadgirKarnatakaIndia9999-01-01 00:00:00.000A1202400000610978218320240003
3023691YadgirYadgiri Women PS0004/202420242MahxxxxxeshMahesh30.0NaNFinancierMALER/o Bilahar Village,Tq wadageri dist yadgiriYadgirKarnataka,YadgirKarnatakaIndia9999-01-01 00:00:00.000A1202400000810978218320240004
3023692YadgirYadgiri Women PS0005/202420242SudxxxxxdhaSudha33.0REDDYLecturerFEMALER/o Opposite Mini Vidansouda yagiri,tq dist YadgiriYadgirKarnataka,YadgirKarnatakaIndia9999-01-01 00:00:00.000A5202400001410978218320240005
3023693YadgirYadgiri Women PS0005/202420242ShaxxxxxmmaShankramma58.0REDDYHousewifeFEMALER/o Opposite Mini Vidansouda yagiri,tq dist YadgiriYadgirKarnataka,YadgirKarnatakaIndiaNaNA2202400001010978218320240005
3023694YadgirYadgiri Women PS0005/202420242ShaxxxxxavaSharnabasava34.0REDDYContractorMALER/o Opposite Mini Vidansouda yagiri,tq dist YadgiriYadgirKarnataka,YadgirKarnatakaIndia9999-01-01 00:00:00.000A1202400001110978218320240005
3023695YadgirYadgiri Women PS0005/202420242GurxxxxxnnaGuranna65.0REDDYRetired PersonMALER/o Opposite Mini Vidansouda yagiri,tq dist YadgiriYadgirKarnataka,YadgirKarnatakaIndia9999-01-01 00:00:00.000A3202400001310978218320240005
3023696YadgirYadgiri Women PS0005/202420242MalxxxxxddyMallareddy36.0REDDYBusinessmanMALER/o Opposite Mini Vidansouda yagiri,tq dist YadgiriYadgirKarnataka,YadgirKarnatakaIndia9999-01-01 00:00:00.000A4202400001510978218320240005
3023697YadgirYadgiri Women PS0006/202420242SanxxxxxnorSantosh Sanna Sabanna Narayananor0.0KABBALIGANaNMALER/o Bandalli,Tq and Dist YadagirYadgirKarnataka,YadgirKarnatakaIndia9999-01-01 00:00:00.000A1202400001610978218320240006

Duplicate rows

Most frequently occurring

District_NameUnitNameFIRNoYearMonthAccusedNamePerson_NameageCasteProfessionSexPresentAddressPresentCityPresentStatePermanentAddressPermanentCityPermanentStateNationality_NameDOBPerson_NoArr_IDcrime_no# duplicates
0Mangaluru CityKavoor PS0027/202220226ANAxxxxxH VANANTH KAMATH V25.0NaNOthers PI SpecifyMALE3-31-2544/88 FLOT NO 208, BHUVANENDRA APARTMENTS,KADRIMangaluru CityKarnataka,Mangaluru CityKarnatakaIndiaNaNA22022000128109801052202200272
1Mangaluru CityKavoor PS0027/202220226CHExxxxxHANCHETHAN28.0NaNOthers PI SpecifyMALE3/4/A ANMQUARTERS, NEAR PANCHAYATH OFFICE,HEJAMADIMangaluru CityKarnataka,Mangaluru CityKarnatakaIndiaNaNA32022000342109801052202200272
2Mangaluru CityKavoor PS0027/202220226LATxxxxxESHLATHESH27.0NaNOthers PI SpecifyMALE1-11 NARANTODI HOUSE, BOLANTURU,BANTWALAMangaluru CityKarnataka,Mangaluru CityKarnatakaIndiaNaNA12022000127109801052202200272
3UdupiUdupi Town PS0151/2022202210ChaxxxxxariChandra Achari42.0NaNOthers PI SpecifyMALEKalmargi, Shiriyara,Shiriyara Village, Brahmavara TalukUdupiKarnataka,UdupiKarnatakaIndiaNaNA82023000411104691197202201512
4UdupiUdupi Town PS0151/2022202210NavxxxxxeenNaveen38.0NaNOthers PI SpecifyMALEH No 6-64, Seetha Nilaya,Doddahitlu, GangolliUdupiKarnataka,UdupiKarnatakaIndiaNaNA122023000415104691197202201512
5UdupiUdupi Town PS0151/2022202210PraxxxxxathPraveen Yakshimath36.0NaNOthers PI SpecifyMALEYakshimutt Road, Gundmi,Sasthana, Brahmavara TalukUdupiKarnataka,UdupiKarnatakaIndiaNaNA92023000412104691197202201512
6UdupiUdupi Town PS0151/2022202210PraxxxxxlliPrakash Kukkehalli36.0NaNOthers PI SpecifyMALEEsha Nilaya, Doddanagudde Cross,Kukkehalli VillageUdupiKarnataka,UdupiKarnatakaIndiaNaNA32023000406104691197202201512
7UdupiUdupi Town PS0151/2022202210PraxxxxxyakPrashanth Nayak44.0NaNOthers PI SpecifyMALEGayathri Building, Market Road,Kukkundoor , KarkalaUdupiKarnataka,UdupiKarnatakaIndiaNaNA22023000405104691197202201512
8UdupiUdupi Town PS0151/2022202210RajxxxxxilaRajesh Uchchila38.0NaNOthers PI SpecifyMALEMuktha Nivasa, Subhash Road,Uchchila, Kapu TqUdupiKarnataka,UdupiKarnatakaIndiaNaNA62023000409104691197202201512
9UdupiUdupi Town PS0151/2022202210RamxxxxxttyRamesh Shetty45.0ACHARIOthers PI SpecifyMALEH No 1-143/4, Hari Guru, Thantribettu,Bailooru Post, Neere VillageUdupiKarnataka,UdupiKarnatakaIndiaNaNA132023000416104691197202201512